Mapping curbstones in airborne and mobile laser scanning data

The high point densities obtained by today's laser scanning systems enable the extraction of various features which are traditionally mapped by photogrammetry or land surveying. While significant progress has been made in the extraction of buildings and trees from dense point clouds, little research has been performed on the extraction of roads. In this paper it is analysed to what extent road sides can be mapped in point clouds of high point density. In urban areas curbstones are often used to separate the road surface from the adjacent pavement. These curbstones are mapped in a three step procedure. First, the locations with small height jumps near the terrain surface are detected. Second, midpoints of high and low points on either side of the height jump are generated, put in a sequence to obtain a polygonal chain describing the approximate curbstone location. A sigmoidal function is then fitted to all points near the polygonal chain to increase the accuracy. Third, small gaps between nearby and collinear line segments are closed. GPS measurements were taken to analyse the performance of the road side detection. The analysis showed that the completeness in airborne laser scanning (ALS) data varying between 53% and 92% is higher than that in mobile laser scanning (MLS) data ranging from 54% to 83%, depending on the amount of parked cars occluding the curbstones. The RMS value in the comparison with the GPS points measured from ground survey was 0.11 m in ALS data and 0.06 m in MLS data, respectively.

► We detect and model curbstones from both airborne and mobile laser scanning data. ► We examine the quality of measured curbstones. ► Higher completeness is achieved in ALS data than in MLS data. ► Accuracy of measuring curbstones that are visible in the MLS data is higher.